Vocal Biomarkers of Mild-to-Moderate Hearing Loss in Children …pedamp1.asu.edu/wp-content/uploads/2019/05/2018-Pittman... · 2019. 5. 22. · Vocal Biomarkers of Mild-to-Moderate

JSLHR

Research Article

Vocal Biomarkers of Mild-to-ModerateHearing Loss in Children andAdults: Voiceless Sibilants

Andrea L. Pittman,a Ayoub Daliri,a and Lauren Meadowsa

Purpose: The purpose of this study was to determine if anobjective measure of speech production could serve as avocal biomarker for the effects of high-frequency hearingloss on speech perception. It was hypothesized thatproduction of voiceless sibilants is governed sufficientlyby auditory feedback that high-frequency hearing lossresults in subtle but significant shifts in the spectralcharacteristics of these sibilants.Method: Sibilant production was examined in individualswith mild to moderately severe congenital (22 children;8–17 years old) and acquired (23 adults; 55–80 years old)hearing losses. Measures of hearing level (pure-toneaverage thresholds at 4 and 8 kHz), speech perception(detection of nonsense words within sentences), andspeech production (spectral center of gravity [COG] for /s/and /ʃ/) were obtained in unaided and aided conditions.Results: For both children and adults, detection of nonsensewords increased significantly as hearing thresholds improved.

Spectral COG for /ʃ/ was unaffected by hearing loss inboth listening conditions, whereas the spectral COG for/s/ significantly decreased as high-frequency hearing lossincreased. The distance in spectral COG between /s/ and /ʃ/decreased significantly with increasing hearing level. COGdistance significantly predicted nonsense-word detectionin children but not in adults.Conclusions: At least one aspect of speech production(voiceless sibilants) is measurably affected by high-frequency hearing loss and is related to speech perceptionin children. Speech production did not predict speechperception in adults, suggesting a more complexrelationship between auditory feedback and feedforwardmechanisms with age. Even so, these results suggestthat this vocal biomarker may be useful for identifying thepresence of high-frequency hearing loss in adults andchildren and for predicting the impact of hearing loss inchildren.

I n 1911, Etienne Lombard noted that speakers in-crease their vocal output in proportion to the levelof ambient noise in the environment (Lane & Tranel,

1971). In addition to increased vocal output, other subtlevocal changes have been reported including increases infundamental frequency (F0) and vowel duration (Arciuli,Simpson, Vogel, & Ballard, 2014). Reasons for these vocalchanges include a desire to monitor the accuracy of speechproduction through auditory feedback (Arciuli et al., 2014)and to improve the overall speech-to-noise ratio to pre-serve speech intelligibility (Simko, Benus, & Vainio, 2016).Research showing frequency-specific vocal changes in re-sponse to band-limited noise in both humans and animals

(Stowe & Golob, 2013) also support the notion that vocalchanges in response to noise are deliberate and somewhatpredictable. Given that the production of speech relies, inpart, on the self-perception of speech, it may be possible todetect problems with a speaker’s auditory sensitivity fromthe acoustic properties of his or her speech.

Like noise at the ear, the acoustic characteristics ofspeech in children and adults with profound hearing losshave also been found to be significantly altered. For exam-ple, several studies in individuals with profound hearingloss have reported greater F0 variability and higher averageF0 while reading a passage (e.g., Lane et al., 1997; Matthies,Svirsky, Lane, & Perkell, 1994). Much of the research inthis area has focused on the acoustic features of speech incochlear implant users to study the contribution (e.g., pres-ence or absence) of auditory feedback during speech pro-duction. One of the primary goals of nearly every studyof this kind is to identify clinically significant anomalies inspeech production due to the unusual signal that cochlearimplants provide (Baudonck, D’Haeseleer, Dhooge, & Van

aDepartment of Speech and Hearing Science, Arizona StateUniversity, Tempe

Correspondence to Andrea L. Pittman: [email protected]

Editor-in-Chief: Frederick (Erick) GallunEditor: Lori J. Leibold

Received December 15, 2017Revision received May 4, 2018Accepted July 8, 2018https://doi.org/10.1044/2018_JSLHR-H-17-0460

Disclosure: The authors have declared that no competing interests existed at the timeof publication.

Journal of Speech, Language, and Hearing Research • Vol. 61 • 2814–2826 • November 2018 • Copyright © 2018 American Speech-Language-Hearing Association2814

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Lierde, 2011; Baudonck, Van Lierde, D’Haeseleer, &Dhooge, 2011; Campisi, Low, Papsin, Mount, & Harrison,2006; Coelho, Brasolotto, & Bevilacqua, 2015; Coelho,Brasolotto, Bevilacqua, Moret, & Bahmad Junior, 2016;Hocevar-Boltezar, Vatovec, Gros, & Zargi, 2005; Hung,Lee, & Tsai, 2017; Jafari et al., 2017; Lejska, 2004; Matthieset al., 1994, 2008; Valero Garcia, Rovira, & Sanvicens,2010; Van Lierde, Vinck, Baudonck, De Vel, & Dhooge,2005; Zhou & Xu, 2008). Thus, the interest in this area ismotivated, in large part, by a desire to improve the speechproduction of individuals with severe-to-profound hearingloss.

Few studies have examined the speech productionof adults and children with mild to moderate degrees ofhearing loss for the same purpose, probably because ab-normalities in their speech production do not rise to thelevel of clinical significance. That is, the effects of mild-to-moderate hearing loss on speech production may be sosubtle that any anomalies are perceptually transparent toboth the speaker and the listener. Even so, Hung et al.(2017) reported a significantly reduced vowel space in 7- to19-year-old children and adolescents with mild-to-moderatehearing losses compared with age-matched children withnormal hearing, whereas Verhoeven, Hide, De Maeyer,Gillis, and Gillis (2016) reported similar effects in youngerchildren with moderately severe hearing loss.

In related studies, grade school children with mild-to-moderate hearing losses were found to misarticulate orsubstitute fricatives and affricates more than their normal-hearing peers, and these errors increased with increasingdegrees of hearing loss (Elfenbein, Hardin-Jones, & Davis,1994; Gordon, 1987; Tye-Murray, Spencer, & Gilbert-Bedia, 1995). More importantly, children with mild-to-moderate hearing loss produced intelligible speech that, atmost, was judged to be slightly more hoarse and nasalthan that of children with normal hearing, but their speechwas otherwise normal (Elfenbein et al., 1994). Together,these studies suggest that, at a minimum, the acoustic char-acteristics of vowels, fricatives, and vocal quality may varypredictably with degree of hearing loss. If so, one or moreof these vocal characteristics may serve as biomarkers ofhearing impairment and the effects of hearing loss on theperception of speech.

What Is a Biomarker?In 1998, the National Institutes of Health convened

a working group to define biomarkers for health-relatedconditions. They concluded that a biomarker is a “char-acteristic that is objectively measured and evaluated as anindicator of normal biological processes, pathogenic pro-cesses, or pharmacologic responses to a therapeutic inter-vention” (Biomarkers Definition Working Group, 2001).Later, Strimbu and Tavel (2010) provided a thoughtful dis-course on the conceptual framework of biomarkers in clini-cal research. They described a biomarker as an objective,quantifiable characteristic of a biological process that mayor may not be related to a patient’s perception of his or

her medical condition. A clinical end point, on the otherhand, is a metric of the patient’s health and well-beingfrom his or her own perspective. In clinical audiology, thepure-tone audiogram is an example of a biomarker thatquantifies the patient’s hearing sensitivity but, as withmany biomarkers, is often out of alignment with the pa-tient’s perception of the effects of hearing loss (i.e., betteror worse). The clinical end point in this scenario is im-proved hearing ability and communication outcomes fromthe patient’s perspective after medical and/or prostheticintervention. Finally, the pure-tone audiogram showingimproved hearing thresholds in response to treatment servesas a surrogate end point because it is a biomarker that, inthe words of Strimbu and Tavel, “can be trusted to serve asa stand-in for, but not as a replacement of, a clinical end-point” (p. 3).

Like all clinical metrics, biomarkers must satisfythree levels of evidence: measurement validity, internalvalidity, and external validity. Whereas measurement andinternal validity indicate that a biomarker is reproducible,reliable, and accurate, external validity confirms that thelink between the biomarker and the behavioral outcomeis valid. Without this evidence, the use of a biomarker asan indicator of a patient’s condition must be assumed, whichcan lead to flawed conclusions. Strimbu and Tavel (2010)describe a large clinical trial of two drugs that togethereffectively reduce cholesterol levels (biomarker), which wasassumed to lower mortality (clinical end point). However,large-scale clinical end point analyses revealed that thelower cholesterol levels did not, in fact, decrease mortality,suggesting an overreliance on the biomarker as a surrogatefor clinical outcomes.

An example from the field of hearing science can befound in the procedures used to prescribe hearing devicesfor children and adults with hearing loss. The output ofthe hearing device must be adjusted individually for eachuser so that the user is able to perceive speech and otherinput optimally. For air-conduction devices (i.e., hearingaids), the accuracy of the device settings is determined bymeasuring the level of the amplified signal in the ear canalof the user. However, these measures do not indicate thelevel of the amplified signal reaching the brain. To determinewhether or not each adjustment to the device is perceptibleto the user, aided pure-tone hearing thresholds and/or speechperception measures are required. Unfortunately, thesemeasures are time-consuming and impractical for eachof the dozens of hearing aid adjustments that must be made.Thus, an objective, quantifiable biomarker that directlyrelates hearing aid output (surrogate end point) with audi-tory perception (clinical end point) would be a valuableaddition to clinical audiology.

Vocal Biomarkers of Hearing LossThe notion of vocal biomarkers for hearing loss is

not entirely new. Coelho, Bevilacqua, Oliveira, and Behlau(2009) showed a direct correlation between improved per-ception of consonants and the mean amplitude (r = .49)

Pittman et al.: Vocal Biomarkers of Hearing Loss 2815


and standard deviation (r = .50) of the F0 of the vowelsproduced by children after 3 years of cochlear implant use.Their results suggest that stable vowel production (vocalbiomarker) was associated with better consonant percep-tion (clinical end point). Tye-Murray et al. (1995) reportedsignificant correlations between information transmissionvia cochlear implants and the nasality (r = .54), place(r = .55), and voice (r = .66) features of speech producedby young children with at least 2 years of cochlear implantexperience. Finally, Blamey et al. (2001) found that, aftercontrolling for vocabulary knowledge, perception of mono-syllabic words in 4- to 13-year-old children with severe-to-profound hearing loss was significantly related to theirconsonant/vowel production. Although only a handfulof studies relate speech production to speech perception(Cysneiros, Leal, Lucena, & Muniz, 2016), there is suffi-cient evidence to suggest that acoustic features of speechmay serve as biomarkers for the effects of hearing loss onspeech perception. Because spectral characteristics, espe-cially in the high-frequency region, are considered to beamong the most perceptually salient acoustic features ofsibilants (Forrest, Weismer, Milenkovic, & Dougall, 1988;Fox & Nissen, 2005; Jongman, Wayland, & Wong, 2000),the production of voiceless sibilants by children andadults with varying levels of high-frequency hearing wasof particular interest in this project. In addition, the percep-tion of voiceless sibilants is most vulnerable to the effectsof mild-to-moderate hearing impairment (Elfenbein et al.,1994; Stelmachowicz, Pittman, Hoover, Lewis, & Moeller,2004), and the voiceless sibilant /s/ is used extensively inEnglish to denote plurality, possession, and verb tense. Thus,the ability to clearly produce /s/ and /ʃ/ facilitates thespeech perception of others.

An important factor contributing to the overall spec-tral characteristics of sibilants is the length of the oralcavity in front of constrictions created during the produc-tion. Typically, a shorter anterior oral cavity results inhigh-frequency spectral peaks, whereas a longer anteriorcavity results in low-frequency spectral peaks (for a review,see Stevens, 2000). For example, the alveolar sibilant /s/has a short anterior oral cavity resulting in a high-frequencyspectral peak (typically > 5 kHz), whereas the palato-alveolar /ʃ/ has a long anterior cavity resulting in a low-frequency spectral peak (typically < 5 kHz). It has beenshown that the spectral characteristics of sibilants are cru-cial for distinguishing different sibilants from one another(Forrest et al., 1988; Fox & Nissen, 2005; Jongman et al.,2000) and for accurately producing the sibilants (Casserly,2011; Shiller, Gracco, & Rvachew, 2010; Shiller, Sato,Gracco, & Baum, 2009). For example, Shiller et al. (2009)used an auditory–motor adaptation paradigm, in whichparticipants were asked to produce sibilants while, in realtime, they heard an experimentally manipulated versionof their productions (i.e., the spectral peak of their pro-duction was experimentally manipulated and fed back tothem in real time). Shiller et al. (2009) showed that normallyhearing individuals adaptively (and subconsciously) chan-ged the spectral shape of their productions when they were

repeatedly exposed to experimental manipulations. More-over, Shiller et al. (2009) showed that the manipulationsof the spectral peak of sibilants resulted in not only changesin production but also changes in the participants’ percep-tion of the sibilants produced by other speakers. Given theimportance of high-frequency spectral characteristics toboth the production and perception of sibilants, it is possi-ble that high-frequency hearing loss might alter their pro-duction and perception significantly. If so, the spectralcharacteristics of sibilants may serve as vocal biomarkersfor high-frequency hearing loss and, more importantly,for the perception of speech in these individuals.

PurposeThe purpose of this study was to determine if a talker’s

vocal productions vary in a predictable fashion with hisor her hearing thresholds and speech perception. The con-ceptual framework for this study is shown in Figure 1. Thefigure shows that the relation between hearing thresholds(surrogate end point) and speech perception (clinical endpoint) is well established whereas the relation of each tovocal production (biomarker) is unknown. It was hypothe-sized that a talker’s production of voiceless sibilants will bealtered in the presence of high-frequency hearing loss andthat the extent of those vocal alterations will directly relateto the talker’s perception of speech. To test this hypothesis,the unaided and aided vocal productions of children andadults with hearing loss were evaluated relative to their hear-ing thresholds and to their speech perception in unaidedand aided listening conditions.

MethodParticipants

Participants were 25 adults (55–80 years old, M =70.0 years old) and 23 children (8–17 years old, M =12.4 years old) with mild to moderately severe, sensorineural

Figure 1. Conceptual framework for the examination of thevocal biomarker (sibilant production) relative to a surrogateend point (hearing thresholds) and a clinical end point (speechperception).

2816 Journal of Speech, Language, and Hearing Research • Vol. 61 • 2814–2826 • November 2018


hearing loss. Each participant completed a speech pro-duction task, although the recordings for two adults andone child were of poor quality and could not be used. Thisreduced the total number of participants to 23 adults and22 children. All participants were native speakers of AmericanEnglish. Before participation, otoscopic examination andacoustic admittance testing were conducted to rule outactive outer and middle ear pathology causing temporaryconductive hearing loss. Hearing levels were determinedthrough pure-tone air- and bone-conduction thresholdaudiometry using insert earphones at octave frequenciesbetween 0.25 and 8 kHz in each ear as well as binaurallyin the free field. Demographic, hearing history, and hearingthresholds for the right and left ears are provided for eachparticipant in Appendixes A and B. Figure 2 shows theaverage and individual unaided and aided hearing thresh-olds obtained binaurally in the free field for the childrenand adults (thresholds at 8 kHz were not obtained forone child). Given that the purpose of this study was toexamine the influence of high-frequency hearing loss onspeech production and perception, high-frequency hear-ing sensitivity was represented as the average of the pure-tone hearing thresholds (pure-tone average [PTA]) for 4and 8 kHz.

For the aided test conditions, participants were fittedbilaterally with Oticon Alta RIC mini BTE hearing aids withnoncustom domes. The hearing aids were used during test-ing only. The output of the devices was frequency-shapedaccording to Desired Sensation Level targets for soft, aver-age, and loud speech levels appropriate for adults and chil-dren (Bagatto et al., 2005) and verified using real-earmeasures.

This study was approved by the institutional reviewboard at Arizona State University. Written consent wasobtained from each adult participant, whereas informedassent was obtained from the children in addition to writtenconsent from their parents. Participants were paid $25per hour for their participation.

Speech PerceptionSpeech perception was evaluated using a revised

version of the Nonword Detection Task developed byPittman and Schuett (2013). In the original version ofthe task, listeners counted the number of nonsense wordsin a short sentence. The revised version required the lis-tener to categorize each word in a four-word sentenceas real or nonsense. Participants selected numbered but-tons corresponding to the position of the word(s) theyconsidered to be nonsense. In this way, identification ofreal and nonsense words could be determined. The non-sense words were created by substituting one phonemefor another in one or two words. Phoneme substitutionswere limited to consonants that preserved the phono-tactic probability of the original word (Vitevitch & Luce,2004). For example, the sentence “Cooks makes hotfoom” was originally “Cooks make hot food.” The posi-tion of the nonsense words within the sentences was dis-tributed evenly across the four word positions. Ten listsof 12 sentences were compiled for this project. Four ofthe sentences contained no nonsense words, four con-tained one nonsense word, and the remaining four sen-tences contained two nonsense words. The sentenceswere recorded using a female speaker with a standardAmerican English dialect.

Testing was conducted in a sound-treated booth.The sentences for the perception task were presented viaa loudspeaker at 65 dB SPL relative to the calibratedposition in the free field. Participants were seated 1 mfrom the loudspeaker at 0° azimuth. Custom laboratorysoftware was used to present the sentences and keeprecord of the participants’ responses. The participantswere instructed to identify the position of each nonsenseword in a sentence by selecting one or more buttons rep-resenting the first to fourth words in each sentence andthen proceeding to the next sentence by selecting the“next” button. Testing was administered first withoutamplification, then with amplification, and again with-out amplification. Performance was calculated as thepercentage of nonsense words correctly identified in eachlistening condition.

Figure 2. Binaural pure-tone hearing thresholds obtained in thesound field for the adults (upper) and children (lower) in the unaided(left) and aided (right) listening conditions. Filled circles connectedby thin lines represent individual participants. Filled squaresconnected by thick lines are average group thresholds.



Speech ProductionSpeech production was recorded in the same sound-

treated booth. Participants were instructed to producespecific phonemes and sentences displayed on a computermonitor. Sustained productions of the phonemes /s/ and /ʃ/were achieved by displaying each phoneme for 5 s andasking the participants to produce the phoneme for as longas it was visible. The same sibilants were produced in thecontext of four monosyllabic words selected from items inthe Expressive Vocabulary Test–Second Edition (Williams,2007). The words were “fish,” “trash,” “sock,” and “nest”embedded in the carrier phrase “Say _______ again.”These words are typically familiar to children by the ageof 7 years. Productions were recorded at a sampling fre-quency of 44100 Hz using a digital audio recorder (Zoom,H2N) coupled to an ear-level microphone on a wire head-set frame. To prevent interference with the hearing aids,the headset frame was placed on the participant’s shouldersat the neck and secured with a 12-in. lightly weightedcloth bag. The microphone was positioned 2 in. from thecorner of the participant’s mouth out of the breath stream.One production of each phoneme and sentence was ob-tained, first without amplification, then with amplification,and then again without amplification.

Data analyses were carried out using Praat (Boersman& Weenink, 2013) and custom MATLAB scripts. Eighteenindividual tokens per participant were entered into thedata analyses: six tokens under the first unaided condition,six tokens under the aided condition, and six tokens underthe second unaided condition. Of the six tokens, three were/s/ productions and three were /ʃ/ productions. The tokensfor each sibilant included one sustained production in isola-tion and two productions in context. Before analysis, allproductions were inspected to confirm the quality ofthe signals and to exclude trials with errors in production(approximately 1% of the total trials were excluded). Thesibilants were then manually annotated and extracted.To remove the influence of low-frequency energy, the ex-tracted signals were high-pass filtered below 0.5 kHz. Eachproduction was segmented into several successive 50-mswindows (0% overlap) for which the spectral center of grav-ity (COG) was calculated. Spectral COG is a statisticalmeasure that corresponds to the central tendency of thespectrum, as shown in Figure 3 for the sibilants /s/ (blue)and /ʃ/ (green), calculated for a single 50-ms window. Spec-tral COG is computed by calculating the discrete Fouriertransform of the signal, weighting each frequency by itsenergy divided by the total energy, and then adding togetherthe weighted frequencies over the entire spectrum. Recentstudies have shown that COG values vary throughout theduration of sibilants and their trajectories may differ acrosssibilants (Iskarous, Shadle, & Proctor, 2011; Reidy, 2016;Reidy, Kristensen, Winn, Litovsky, & Edwards, 2017).Given the temporal trajectories of sibilants and giventhat the production tasks included sustained and isolated(in-context) tokens with different temporal characteristics,it was appropriate to average COG values across all windows.

In addition, we compared midpoint COG values (i.e., takenfrom a 50-ms window placed in the middle of each token)with average COG values; our results showed that there wasa close agreement between these two types of measurements(r = .968, p < .001). Therefore, COG values for each tokenwere averaged across all windows to generate the final COGvalue for that token. Given that (a) COG values are depen-dent on vocal tract size and (b) the goal of this study was toexamine changes in the spectral characteristics of sibilants inindividuals with high-frequency hearing loss, we calculatedCOG distance between /s/ and /ʃ/ tokens for each participant.This within-participant measure ensured normalization of dataacross participants. Figure 3 shows the spectral distance (inhertz) between the COG values for these tokens. It shouldbe noted that, in this study, we analyzed the data for the twogroups separately because of the physical, developmental,and cognitive differences between children and adults. Thus,we did not compare the productions of the children andadults statistically (see Discussion).

ResultsSpeech Perception

The percentage of nonsense words correctly detectedwithin each set of 12 sentences was calculated for each par-ticipant. Figure 4 shows the average performance (±1 SE )in the first and second unaided conditions for the childrenand adults. Given that the percentage scale is bounded, itdoes not satisfy the assumption of homogeneity of vari-ance. Thus, we used arcsine transform to equalize the vari-ance (Fox, 2015). All of the statistical procedures wereapplied to the arcsine transformations of the nonword de-tection scores. One-sample t tests revealed no differences inperformance between the two unaided conditions for the

Figure 3. Example of spectral center of gravity (COG) for /s/ (blue)and /ʃ/ (green) productions. Normalized power for a single 50-mswindow is shown as a function of frequency. The vertical arrows showthe COG for each sibilant, whereas the horizontal line representsthe distance between the two COGs in hertz.



children, t(21) = −0.905, p = .375, or the adults, t(24) =−1.874, p = .073. Thus, performances of each individualin the two unaided conditions were averaged together beforestatistical analyses.

Overall, performance (detection of both real and non-sense words) decreased significantly with increasing hear-ing loss for the children, r(44) = −.45, p = .001, and for theadults, r(46) = −.46, p = .001. A slightly higher correlationwas found for both the children, r(44) = −.68, p < .001,and adults, r(46) = −.57, p < .001, for the detection of thenonsense words only. These scores were used for all furtheranalyses. Figure 5 shows detection of the nonsense wordsas a function of PTA for each group in the unaided andaided conditions. Although the scores for the adults appearto exceed those of the children, no statistically signifi-cant effect of age group was found in either the unaided,F(1, 46) = 0.612, p = .438, ηp

2 = .12, or aided, F(1, 46) =0.486, p = .494, ηp

2 = .106, listening conditions. Thus, per-formance on the revised Nonword Detection Task variedsimilarly and predictably with hearing loss for both groupsand could therefore be used as a measure of speech percep-tion to test the hypothesis.

Speech ProductionThere were no significant differences between COG

values in the two unaided conditions for the children’s oradults’ production of /s/ or /ʃ/. Therefore, the COG valuesin the two unaided conditions were averaged together be-fore statistical analyses. Figure 6 shows COG values for/s/ and /ʃ/ produced by each participant in the context ofthe words (averaged over two tokens) as a function of pro-duction in isolation (one token each). Spectral COG in theunaided (upper) and aided (lower) conditions are shown

for each phoneme produced by the children and adults.The ellipses were calculated using principal componentanalysis and are displayed for each phoneme and groupas 2 SDs around the mean. The diagonal line in each panelrepresents perfect agreement between the COG values ofeach phoneme produced in context and in isolation. Thefigure shows that, as expected, the children’s overall COGis higher in frequency than that of the adults for both pho-nemes in the unaided and aided conditions. In addition,the expected distance in COG between the /s/ and /ʃ/ pho-nemes is observed, although there is an overlap in the 3- to5-kHz region. To the extent that these participants repre-sent the pediatric and adult populations, productions wereconsidered to be equivalent in both contexts. Therefore,the COG values for each sibilant of each participant wereaveraged for the remaining analyses (i.e., averaged acrossthe two tokens in context and one in isolation).

Figure 4. Average (±1 SE ) performance for the nonsense wordson the revised Nonword Detection Task in the first and secondunaided listening conditions for the adults (blue) and children(yellow).

Figure 5. Nonword detection as a function of pure-tone average(PTA) hearing level for the adults (blue) and children (yellow) in theunaided (upper) and aided (lower) listening conditions. Data fromtwo adult participants, represented with filled squares, were notincluded in the production task.



To accommodate high-frequency hearing loss, aspeaker may alter the production of /s/ and /ʃ/ in severalways. For example, the spectral energy of /s/ could beshifted to a lower frequency region with better residualhearing, or the spectral energy of /ʃ/ could be shifted toa higher frequency region for the same reason. To deter-mine the direction of change, the relation between COGand PTA hearing level was examined for both sibilants. Asshown in Figure 7, the children’s COG for /ʃ/ decreasedslightly with increasing hearing loss in both the unaided,r(22) = −.43, p = .049, and aided, r(22) = −.45, p = .04,conditions, but the adults’ COG remained unchanged inthe unaided, r(23) = −.32, p = .14, and aided, r(23) = −.19,p = .39, conditions. Similarly, the children’s COG for /s/

decreased as hearing loss increased in both of the unaided,r(22) = −.80, p < .001, and aided, r(22) = −.65, p = .001,conditions, but this pattern only occurred for the adults inthe unaided condition, r(23) = −.42, p = .045, and not inthe aided condition, r(23) = −.28, p = .199. These resultsindicate that spectral COG decreased more for /s/ than for/ʃ/ productions as hearing loss increased causing the spec-tral energy of both phonemes to converge. Thus, this vocalresponse to hearing loss was represented as the distance(in hertz) between the COG of /s/ and /ʃ/ in the statisticalanalyses (see Figure 3 for an illustration of COG distancecalculation).

Linear regression analysis was used to determine ifdemographic factors predicted COG distance. COG dis-tance was entered as the dependent variable, whereas chro-nological age, age at identification of hearing loss, age at

Figure 6. Individual spectral center of gravity values for /s/ (blue)and /ʃ/ (green) produced within words as a function of production inisolation. Productions in the unaided and aided listening conditionsare shown in the upper and lower panels, respectively. The ellipseswere calculated using principal component analysis and are displayedfor each phoneme and group as 2 SDs around the mean.

Figure 7. Individual spectral center of gravity of /s/ and /ʃ/ productionsas a function of hearing level for the adults (blue) and children(yellow) in the unaided (upper panel) and aided (lower panel) listeningconditions. Light and dark shades represent data for children andadults, respectively. PTA = pure-tone average.



amplification, and years of hearing aid use were entered asindependent variables. None of the demographic variables pre-dicted COG distance for either group in the unaided (adults:R = .115, F(4, 17) = 0.423, p = .789; children: R = .314,F(4, 20) = 1.827, p = .173) or aided (adults: R = .119,F(4, 17) = 0.438, p = .779; children: R = .168, F(4, 20) =0.806, p = .539) conditions. Because the adults representedmore variability in hearing aid experience than the children,amplification history was recoded to represent “new” and“experienced” hearing aid users; however, the regressionmodel was not significant with this new variable in eitherthe unaided or aided conditions.

COG Distance as a Vocal Biomarkerfor Hearing Loss

Figure 8 shows COG distance as a function of PTAhearing threshold for the children and adults in the un-aided and aided conditions. COG distance decreased sig-nificantly with increasing hearing loss for the children inboth the unaided, r(22) = −.79, p < .001, and aided, r(22) =−.51, p = .008, conditions, whereas no statistically signif-icant relationship was found in either condition for theadults (unaided: r(23) = −.35, p = .05; aided: r(23) = −.18,p = .21). These results indicate that the children’s speech pro-duction was significantly related to their degree of hearingloss. Specifically, their /s/ and /ʃ/ productions became spec-trally similar as the degree of hearing loss increased.

Multiple regression analysis was used to test if COGdistance significantly predicted the participants’ PTA hear-ing thresholds at 4 and 8 kHz. Separate analyses wereconducted for the children and adults. PTA hearing thresh-olds served as the dependent variable, whereas COG dis-tance, chronological age, age at identification of hearingloss, age at amplification, years of hearing aid use, and lis-tening condition (unaided or aided) were independent vari-ables entered into the model in a stepwise fashion. Notethat nonsense-word detection was not included in the anal-ysis because its relationship to COG distance was evalu-ated separately (below). For the children, the results of theregression indicated that two predictors explained 56%of the variance, R = .745, F(2, 39) = 24.36, p < .001. Itwas found that hearing thresholds were significantly pre-dicted by COG distance, β = −.551, p < .001, and listeningcondition, β = −.54, p < .001. All other variables wereexcluded in the analysis. For the adults, the results of theregression also indicated that two predictors explained54% of the variance, R = .736, F(2, 33) = 19.558, p < .001.However, in this group, hearing thresholds were not pre-dicted by COG distance but by listening condition, β = −.672,p < .001, and age of identification, β = −.301, p = .015. Allother variables were excluded in the analysis.

COG Distance as a Vocal Biomarkerfor Speech Perception

Figure 9 shows COG distance as a function of non-sense-word detection for each participant and listening

condition, using the same convention as in the previousfigures. Recall that the data were arcsine transformed fordata analyses. However, for visual presentation purposes,the calculated regression lines were transformed back tothe percentage scale. The results indicate a predictive rela-tionship between speech perception and speech productionfor the children but not for the adults. Specifically, chil-dren with spectrally distinct productions of /s/ and /ʃ/ werebetter able to identify nonsense words embedded into sen-tences than children whose productions of /s/ and /ʃ/ werespectrally similar. The spectral characteristics of the adults’/s/ and /ʃ/ productions do not appear to be related to theirspeech perception in either of the listening conditions.

Multiple regression analysis was used to test if COGdistance significantly predicted the participants’ detectionof nonsense words. Separate analyses were conducted for

Figure 8. Individual spectral center of gravity distance (/s/–/ʃ/) as afunction of hearing level for the adults (blue) and children (yellow)in the unaided (upper) and aided (lower) listening conditions. PTA =pure-tone average.



the children and the adults. Nonsense-word detectionserved as the dependent variable, whereas COG distance,chronological age, age at identification of hearing loss,age at amplification, years of hearing aid use, and listen-ing condition (unaided or aided) were independent vari-ables entered into the model in a stepwise fashion. Notethat PTA hearing levels were not included in the analysisbecause their relationship to COG distance had alreadybeen established (above). For the children, the results ofthe regression indicated that two predictors explained 25%of the variance, R = .497, F(2, 39) = 6.41, p = .004. It wasfound that nonsense-word detection was significantly pre-dicted by COG distance, β = .17, p = .01, and by listeningcondition, β = .27, p = .011. Likewise, the regression indi-cated that two predictors explained 49% of the variance

for the adults, R = .702, F(2, 33) = 16.037, p < .001. Non-sense-word detection was not predicted by COG distancebut by years of amplification, β = −.603, p < .001, and bylistening condition, β = .36, p = .007. However, years ofamplification and nonsense-word detection were negativelyassociated, indicating that performance decreased as yearsof hearing aid use increased.

DiscussionRecall that the purpose of this project was to deter-

mine if an objective measure of speech production couldserve as a vocal biomarker for the effects of hearing loss.The first step toward this goal was to identify a measureof speech perception (clinical end point) that varied pre-dictably with hearing level (surrogate end point). The Non-word Detection Task was selected because the perceptionof unfamiliar words requires greater auditory acuity thanthe perception of familiar words. In addition, performancefor this task can be scored automatically rather than re-quiring a subjective assessment of the speaker’s verbal rep-etition of real or nonsense words. The results showed thatnonsense-word detection decreased significantly with in-creased degree of hearing loss at 4 and 8 kHz for both thechildren and adults in the unaided and aided listening con-ditions. Thus, the first criterion was satisfied.

The next step was to identify a vocal biomarker thatalso varied predictably with hearing loss. Because thespectral energy of the voiceless sibilants /s/ and /ʃ/ occursin the high-frequency region, the spectral COG for theseproductions was calculated. The results showed that, ingeneral, the COG for /s/ decreased systematically with in-creasing hearing loss whereas the COG for /ʃ/ was rela-tively unaffected by hearing level. As a result, the spectralCOG for /s/ and /ʃ/ converged for participants with moresevere hearing losses but not for participants with milderlosses. Thus, the sensitivity of COG distance for predictinghearing level satisfied the second criterion.

The final step was to determine if the vocal biomarkerwas related to the surrogate and clinical end points: hear-ing level and speech perception. COG distance was foundto significantly predict hearing level and nonsense-worddetection in children but not in adults. A closer examina-tion of the results suggests that the predictive power ofthe vocal biomarker may have been poorer in adults be-cause nonsense-word detection was not as closely alignedwith hearing loss in adults as it was in children. The sensi-tivity of COG distance for predicting hearing level andspeech perception in children satisfied the third and finalcriterion.

Taken together, these results suggest that a vocalbiomarker such as sibilant production can be used to pre-dict hearing thresholds and speech perception in childrenbut not in adults. Interestingly, factors such as age at iden-tification of hearing loss, age at amplification, and yearsof hearing aid use did not contribute significantly to thevariability in the children’s data. However, age of identifi-cation predicted hearing thresholds and years of hearing

Figure 9. Individual spectral center of gravity distance (/s/–/ʃ/) asa function of nonword detection for the adults (blue) and children(yellow) in the unaided (upper) and aided (lower) conditions. Linearregressions were applied to the arcsine transformations of thenonword detection, and regression lines were transformed backto percentage for visual presentation.



aid use predicted speech perception in adults. Perhaps,the smaller range of ages in the children (0–11 years) com-pared with the adults (20–75 years) was too brief a timeto establish a relationship between the biomarker and thesurrogate/clinical end points. Similarly, years of hearingaid use varied more for the adults (0–25 years) than for thechildren (0–11 years).

Implications for Speech Motor ControlThe larger decrease in COG for /s/ with increasing

high-frequency hearing loss for the children compared withthe adults (see Figure 7) points to a potential influence ofage of hearing loss acquisition on sibilant production.During speech acquisition, the central nervous system es-tablishes sensory targets and mappings between feedforwardcommands and their sensory consequences. Given thatspeech acquisition is heavily reliant on auditory feedbackespecially during early development of speech, hearingimpairment can substantially affect speech production (fora review, see Perkell, Lane, Svirsky, & Webster, 1992).However, the role of auditory feedback may graduallychange during the transition from childhood to adulthood(Callan, Kent, Guenther, & Vorperian, 2000; Guenther,2006; Kuhl, 1994, 2004; Tourville & Guenther, 2011), andthus the reliance on auditory feedback may be different foradults than for children. One potential explanation for thedifference between adults and children is that (a) childrenmay rely on feedback mechanisms more than adults and(b) children with hearing loss may have less robust andreliable feedforward mechanisms as their feedforwardmechanisms are shaped by their impaired auditory feed-back. Therefore, greater high-frequency hearing loss ismore likely to influence the speech production of childrenthan that of adults. Adults with acquired hearing loss, onthe other hand, learned to speak when they had normalhearing, resulting in robust and reliable feedforward mecha-nisms that are less likely to change in response to hearingloss. This is particularly true for segmental features ofspeech (e.g., Perkell, 2012) such as the spectral COGused in this study. This explanation is in general agreementwith studies that have shown that the segmental featuresof speech of adults with postlingual hearing impairmentremain relatively stable, even several years after deafness(Lane et al., 1997; Matthies et al., 1994; Perkell et al.,1992). Another related explanation is that (a) sibilants havestrong somatosensory targets (e.g., Ghosh et al., 2010), and(b) relative to adults, children with hearing loss may relyless on somatosensory feedback because of failure to estab-lish robust and reliable somatosensory targets duringspeech acquisition. Overall, the combination of a less reli-able somatosensory feedback and a less reliable feedforwardmechanism in children with hearing loss could lead tomore reliance on auditory feedback. Therefore, increasedhearing loss is more likely to change the speech productionof children than the speech production of adults. However,alternative explanations cannot be ruled out without furtherexamining reliance on auditory feedback, somatosensory

feedback, and feedforward mechanisms in both childrenand adults with and without hearing loss.

Another intriguing aspect of these results is the ab-sence of an immediate effect of amplification on the pro-duction of sibilants in both children and adults. This resultis particularly puzzling given that the amplification im-proved the participants’ hearing thresholds and speechperception significantly. The lack of immediate effect ofamplification on speech production may be related to com-plex contributions of auditory feedback in speech produc-tion. Although participants in this study were aware ofthe amplification they received, it is possible that the dura-tion of amplification was too brief to provoke a reliableand measurable adaptive change in sibilant production.In addition, the hearing aids used to optimize perceptualperformance were not familiar to the participants andmay have introduced uncertainty in the auditory feedback,causing both the children and adults to rely more heavilythan usual on somatosensory feedback or feedforwardmechanisms for sibilant production. To more systemati-cally examine the effects of hearing loss on feedbackand feedforward mechanisms, within-trial changes in thespectral characteristics of sibilants could be examinedfor random and unexpected changes in amplification (i.e.,examining feedback mechanisms) or adaptive changesin the spectral characteristics of sibilants after repeatedexposure to amplification (i.e., examining feedforwardmechanisms). Together, these possibilities suggest thatthe nature of the compensatory strategies of children andadults with hearing loss may be a fruitful area of furtherresearch.

Implications for Clinical ApplicationAs mentioned in the introduction, ambiguity in

the production of /s/ and /ʃ/ has implications for both thespeaker and the listener. Although the impact of mild-to-moderate hearing loss on the speech production of adultsand children is generally not clinically significant, theresults of this study suggest that clarity of speech may bereduced in the presence of hearing loss. In addition, thatreduced clarity appears to be independent of the age whenthe hearing loss occurred.

Also mentioned in the introduction, vocal bio-markers that relate hearing aid output (surrogate endpoint) with auditory perception (clinical end point) wouldbe a valuable addition to clinical audiology. Such bio-markers would be useful when other surrogate end points,such as pure-tone thresholds, are not available or aretoo costly to obtain because of time, expense, or both.Although additional biomarkers will need to be identified,those that provide frequency-specific information couldbe included in the fitting process for a wide range of hear-ing devices including air-conduction hearing aids, bone-anchored devices, and cochlear implants. An area offurther research is the identification of vocal biomarkersthat correspond to hearing levels in the low- and mid-frequency regions.



It is worth noting that the vocal biomarker identifiedin this study was derived from a small number of speechtokens obtained within a short time (3 tokens × 2 sibilants ×3 conditions × ~1000 ms = 18 s). This uncharacteristicallysmall sample of tokens was motivated by the potentialapplication of vocal biomarkers in clinical settings. Althoughthe validity of this vocal biomarker was demonstratedfor children, it is possible that the predictive accuracy ofthe biomarker might improve for adults if more samplesare collected. Even a doubling of samples would requirelittle additional time and effort without sacrificing clinicalefficiency.

ConclusionThe results of this study revealed an aspect of speech

production (voiceless sibilants) that is measurably relatedto degree of hearing loss and to speech perception in chil-dren with mild to moderately severe hearing loss. This rela-tionship was not observed in adults but does not rule outthe possibility of identifying other vocal biomarkers. Theresults suggest that voiceless sibilants can be used as vocalbiomarkers for identifying the presence and degree of high-frequency hearing loss and the effect of that hearing loss onspeech perception. This and other vocal biomarkers thatprovide objective verification of treatment (e.g., hearing aidbenefit) would be valuable additions to patient care.

AcknowledgmentsThis study was funded by a grant from the Oticon Foun-

dation, awarded to the first author. The authors would like tothank Drs. Elizabeth Stewart, Ashley Wright, and Jacelyn Olsenfor their help with data collection.

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Appendix A

Age, Gender, Hearing History, and Hearing Thresholds for the Right and Left Ears of the Adult Participants

Appendix B

Age, Gender, Hearing History, and Hearing Thresholds for the Right and Left Ears of the Child Participants

# Age GenderAgeat ID

Ageat Amp

Years ofHA use

Right ear Left ear

0.25 0.5 1 2 4 8 0.25 0.5 1 2 4 8

1 74 M NI — 0 40 30 40 45 85 100 30 15 20 50 60 852 62 M 57 — 0 10 15 15 40 50 80 15 15 15 35 55 804 69 M 69 — 0 20 15 20 20 70 70 20 15 10 10 60 405 79 F 75 75 3.5 15 40 60 60 70 75 20 35 45 55 65 756 70 F 69 69 1 10 20 20 20 10 35 15 25 20 20 40 457 72 M 65 65 7 40 40 45 50 50 75 40 45 35 50 55 608 64 M 64 — 0 25 30 25 35 55 80 35 35 30 25 45 509 76 M NI NI NI 50 60 60 90 105 100 90 55 55 75 105 9010 55 F 50 55 0.1 25 15 20 30 35 55 15 15 20 35 45 4511 70 M 66 — 0 5 5 25 35 35 40 5 10 20 25 30 3512 70 M 70 — 0 10 10 10 25 65 70 0 5 15 25 60 6013 67 F 52 — 0 30 30 30 40 55 90 30 35 35 40 60 8514 78 M 10 68 10 20 55 65 70 85 80 25 50 70 75 70 7515 73 F 67 67 6 25 30 45 55 65 65 30 40 45 55 80 8016 54 F 20 32 22 60 65 65 55 85 95 60 70 75 75 95 10517 71 F 46 46 25 50 50 50 60 50 90 60 60 50 55 55 8518 74 F 62 62 12 55 60 60 60 60 65 55 55 60 55 60 7020 69 M 65 — 0 15 15 10 15 55 75 15 15 5 10 55 7521 69 M 68 — 0 10 10 10 20 35 55 10 10 25 35 55 5522 70 M 68 — 0 25 20 40 40 65 95 35 40 45 45 70 7523 67 M 25 65 1.5 15 20 25 55 75 50 15 20 25 55 80 6024 68 F 63 — 0 45 45 35 45 40 60 50 45 40 35 25 4025 73 F 44 44 29 5 15 40 60 65 65 10 20 30 55 60 70

Note. Em dashes indicate no history of hearing aid use. ID = identification of hearing loss; Amp = amplification; HA = hearing aid; M = male;F = female; NI = no information.

# Age GenderAgeat ID

Ageat Amp

Years ofHA use

Right ear Left ear

0.25 0.5 1 2 4 8 0.25 0.5 1 2 4 8

1 12 F 1 1.5 10 20 25 45 50 55 60 20 30 40 50 60 552 14 F 4.5 5 9 50 60 40 30 20 20 0 5 5 0 5 53 12 F 7 7 5 10 25 50 60 50 10 5 20 45 65 50 154 11 F 11 11 0.5 20 20 25 40 30 40 25 20 15 45 20 305 12 F 0 0.25 13 30 35 40 50 50 40 30 40 45 55 55 36 10 M 1 1.5 9 30 35 45 55 50 45 25 40 45 55 50 407 14 F 3 3 11 20 25 50 50 40 45 35 35 50 50 45 508 13 F 0 0.5 11.5 35 45 50 50 50 45 30 40 50 50 45 459 16 M 0 0 16 30 35 45 50 50 50 25 30 45 45 50 55

10 11 M 0 0 11 35 50 60 60 65 60 35 45 60 65 65 6011 8 M 3 3 5 5 5 5 10 40 35 5 5 10 15 55 2512 11 F 7 7 4 10 5 25 40 25 35 5 10 20 40 25 3013 15 M 3 3 12 35 40 45 50 50 45 30 40 45 50 50 4015 11 F — — 0 0 0 0 0 0 0 50 50 35 25 20 1016 12 M 2 2 10 50 55 60 75 65 55 55 65 70 75 70 5517 11 F 6 8 2 30 35 50 50 30 0 25 35 50 50 10 518 8 F 4 4 4 30 25 45 30 40 15 20 35 45 40 40 3019 12 M 0 1.5 11 50 60 70 75 65 60 55 60 70 75 70 6020 16 F 4 4 12 40 40 35 30 20 20 40 50 35 55 35 3021 10 F 0 0.25 10 15 20 30 50 35 45 15 15 15 20 35 4522 12 M 10 10 2 25 20 45 70 85 100 25 25 50 75 85 10523 15 M 2 2 6 15 15 30 40 35 30 20 25 30 45 35 40

Note. Em dashes indicate no history of hearing aid use. ID = identification of hearing loss; Amp = amplification; HA = hearing aid; M = male;F = female.



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